This workshop is designed to introduce the concepts of unsupervised learning, a branch of machine learning where algorithms infer patterns from unlabelled data. The course covers clustering methods like K-means and DBSCAN, used to identify inherent groupings in data. It also explores dimensionality reduction techniques such as PCA, which simplify complex data sets while preserving their key features. Additionally, the session introduces association rules, a method for finding interesting relationships within data sets. This workshop is ideal for those interested in learning how to extract insights from data without predetermined labels or categories.